jimhester/surveillance: Temporal and Spatio-Temporal Modeling and Monitoring of Epidemic Phenomena

Implementation of statistical methods for the modeling and change-point detection in time series of counts, proportions and categorical data, as well as for the modeling of continuous-time epidemic phenomena, e.g., discrete-space setups such as the spatially enriched Susceptible-Exposed-Infectious-Recovered (SEIR) models, or continuous-space point process data such as the occurrence of infectious diseases. Main focus is on outbreak detection in count data time series originating from public health surveillance of communicable diseases, but applications could just as well originate from environmetrics, reliability engineering, econometrics or social sciences. Currently, the package contains implementations of many typical outbreak detection procedures such as Farrington et al (1996), Noufaily et al (2012) or the negative binomial LR-CUSUM method described in Höhle and Paul (2008). A novel CUSUM approach combining logistic and multinomial logistic modelling is also included. Furthermore, inference methods for the retrospective infectious disease models in Held et al (2005), Held et al (2006), Paul et al (2008), Paul and Held (2011), Held and Paul (2012), and Meyer and Held (2014) are provided. Continuous self-exciting spatio-temporal point processes are modeled through additive-multiplicative conditional intensities as described in Höhle (2009) ('twinSIR', discrete space) and Meyer et al (2012) ('twinstim', continuous space). The package contains several real-world data sets, the ability to simulate outbreak data, visualize the results of the monitoring in temporal, spatial or spatio-temporal fashion.

Package details

URL http://surveillance.r-forge.r-project.org/
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
jimhester/surveillance documentation built on May 19, 2019, 10:33 a.m.